U.S. patent application number 15/856424 was filed with the patent office on 2019-07-04 for systems and methods for notification send control using negative sentiment.
The applicant listed for this patent is Facebook, Inc.. Invention is credited to Daniel Dinu, Qingyuan Kong, Ashish Kumar Yadav.
Application Number | 20190208025 15/856424 |
Document ID | / |
Family ID | 67060017 |
Filed Date | 2019-07-04 |
United States Patent
Application |
20190208025 |
Kind Code |
A1 |
Kong; Qingyuan ; et
al. |
July 4, 2019 |
SYSTEMS AND METHODS FOR NOTIFICATION SEND CONTROL USING NEGATIVE
SENTIMENT
Abstract
Systems, methods, and non-transitory computer readable media are
configured to determine a likelihood of a rejection of a
notification proposed for delivery to a recipient. A delivery
determination for the notification can be performed. Subsequently,
the notification can be delivered to the recipient based on the
delivery determination.
Inventors: |
Kong; Qingyuan; (Union City,
CA) ; Yadav; Ashish Kumar; (Mountain View, CA)
; Dinu; Daniel; (Sunnyvale, CA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Facebook, Inc. |
Menlo Park |
CA |
US |
|
|
Family ID: |
67060017 |
Appl. No.: |
15/856424 |
Filed: |
December 28, 2017 |
Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G06N 5/022 20130101;
G06N 20/00 20190101; H04L 67/306 20130101; H04L 67/22 20130101;
G06Q 50/01 20130101; H04L 67/26 20130101; G06F 16/9535 20190101;
G06F 16/958 20190101 |
International
Class: |
H04L 29/08 20060101
H04L029/08; G06F 15/18 20060101 G06F015/18; G06F 17/30 20060101
G06F017/30; G06Q 50/00 20060101 G06Q050/00 |
Claims
1. A computer-implemented method comprising: determining, by a
computing system, a likelihood of a rejection of a notification
proposed for delivery to a recipient; performing, by the computing
system, a delivery determination for the notification; and
delivering, by the computing system, the notification to the
recipient based on the delivery determination.
2. The computer-implemented method of claim 1, further comprising:
determining, by the computing system, a likelihood of a selection
of the notification.
3. The computer-implemented method of claim 1, wherein the
determining the likelihood of the rejection of the notification is
based on a machine learning model.
4. The computer-implemented method of claim 3, further comprising:
providing, by the computing system, to the machine learning model,
feature data for the notification.
5. The computer-implemented method of claim 3, further comprising:
providing, by the computing system, to the machine learning model,
feature data for the recipient.
6. The computer-implemented method of claim 3, further comprising:
providing, by the computing system, to the machine learning model,
feature data regarding previous delivery of the notification to the
recipient.
7. The computer-implemented method of claim 1, wherein the
performing the delivery determination for the notification is based
at least in part on the likelihood of the rejection of the
notification.
8. The computer-implemented method of claim 7, wherein the
performing the delivery determination for the notification is based
at least in part on the likelihood of the selection of the
notification.
9. The computer-implemented method of claim 8, wherein the
performing the delivery determination for the notification is based
at least in part on a weight applied to the likelihood of the
rejection of the notification.
10. The computer-implemented method of claim 1, wherein the
performing the delivery determination for the notification
comprises: generating a score based at least in part on the
likelihood of the rejection of the notification, a selected weight
applied to the likelihood of the rejection of the notification, and
a likelihood of a selection of the notification; and comparing the
score to a threshold value.
11. A system comprising: at least one processor; and a memory
storing instructions that, when executed by the at least one
processor, cause the system to perform: determining a likelihood of
a rejection of a notification proposed for delivery to a recipient;
performing a delivery determination for the notification; and
delivering the notification to the recipient based on the delivery
determination.
12. The system of claim 11, wherein the instructions, when executed
by the at least one processor, further cause the system to perform:
determining a likelihood of a selection of the notification.
13. The system of claim 11, wherein the determining the likelihood
of the rejection of the notification is based on a machine learning
model.
14. The system of claim 11, wherein the performing the delivery
determination for the notification is based at least in part on the
likelihood of the rejection of the notification.
15. The system of claim 11, wherein the performing the delivery
determination for the notification comprises: generating a score
based at least in part on the likelihood of the rejection of the
notification, a selected weight applied to the likelihood of the
rejection of the notification, and a likelihood of a selection of
the notification; and comparing the score to a threshold value.
16. A non-transitory computer-readable storage medium including
instructions that, when executed by at least one processor of a
computing system, cause the computing system to perform a method
comprising: determining a likelihood of a rejection of a
notification proposed for delivery to a recipient; performing a
delivery determination for the notification; and delivering the
notification to the recipient based on the delivery
determination.
17. The non-transitory computer-readable storage medium of claim
16, wherein the instructions, when executed by the at least one
processor of the computing system, further cause the computing
system to perform: determining a likelihood of a selection of the
notification.
18. The non-transitory computer-readable storage medium of claim
16, wherein the determining the likelihood of the rejection of the
notification is based on a machine learning model.
19. The non-transitory computer-readable storage medium of claim
16, wherein the performing the delivery determination for the
notification is based at least in part on the likelihood of the
rejection of the notification.
20. The non-transitory computer-readable storage medium of claim
16, wherein the performing the delivery determination for the
notification comprises: generating a score based at least in part
on the likelihood of the rejection of the notification, a selected
weight applied to the likelihood of the rejection of the
notification, and a likelihood of a selection of the notification;
and comparing the score to a threshold value.
Description
FIELD OF THE INVENTION
[0001] The present technology relates to computerized sending of
notifications. More particularly, the present technology relates to
techniques for using negative sentiment to control computerized
sending of notifications in a networking system.
BACKGROUND
[0002] Users often utilize computing devices for a wide variety of
purposes. For example, users of a social networking system can use
their computing devices to interact with one another, access
content, share content, and create content. The social networking
system can deliver various notifications to these users. For
example, a given user of the social networking system can
administer a page on the social networking system. The social
networking system can deliver to this user a notification which
encourages the user to perform one or more specified actions with
regard to the page. As another example, a first user of the social
networking can follow a second user on the social networking
system. In this example, the social networking system can deliver
to the first user a notification which informs the first user that
the second user has made a post.
SUMMARY
[0003] Various embodiments of the present disclosure can include
systems, methods, and non-transitory computer readable media
configured to determine a likelihood of a rejection of a
notification proposed for delivery to a recipient. A delivery
determination for the notification can be performed. Subsequently,
the notification can be delivered to the recipient based on the
delivery determination.
[0004] In an embodiment, a likelihood of a selection of the
notification can be determined.
[0005] In an embodiment, determining the likelihood of the
rejection of the notification can be based on a machine learning
model.
[0006] In an embodiment, feature data for the notification can be
provided to the machine learning model.
[0007] In an embodiment, feature data for the recipient can be
provided to the machine learning model.
[0008] In an embodiment, feature data regarding previous delivery
of the notification to the recipient can be provided to the machine
learning model.
[0009] In an embodiment, performing the delivery determination for
the notification can be based at least in part on the likelihood of
the rejection of the notification.
[0010] In an embodiment, performing the delivery determination for
the notification can be based at least in part on the likelihood of
the selection of the notification.
[0011] In an embodiment, performing the delivery determination for
the notification can be based at least in part on a weight applied
to the likelihood of the rejection of the notification.
[0012] In an embodiment, performing the delivery determination for
the notification can further comprise generating a score based at
least in part on the likelihood of the rejection of the
notification, a selected weight applied to the likelihood of the
rejection of the notification, and a likelihood of a selection of
the notification. Subsequently, the score can be compared to a
threshold value.
[0013] It should be appreciated that many other features,
applications, embodiments, and/or variations of the disclosed
technology will be apparent from the accompanying drawings and from
the following detailed description. Additional and/or alternative
implementations of the structures, systems, non-transitory computer
readable media, and methods described herein can be employed
without departing from the principles of the disclosed
technology.
BRIEF DESCRIPTION OF THE DRAWINGS
[0014] FIG. 1 illustrates an example system including an example
notification control module, according to an embodiment of the
present disclosure.
[0015] FIG. 2 illustrates an example prediction module, according
to an embodiment of the present disclosure.
[0016] FIG. 3 illustrates an example decision module, according to
an embodiment of the present disclosure.
[0017] FIG. 4 illustrates an example functional block diagram,
according to an embodiment of the present disclosure.
[0018] FIG. 5 illustrates an example process, according to an
embodiment of the present disclosure.
[0019] FIG. 6 illustrates a network diagram of an example system
including an example social networking system that can be utilized
in various scenarios, according to an embodiment of the present
disclosure.
[0020] FIG. 7 illustrates an example of a computer system or
computing device that can be utilized in various scenarios,
according to an embodiment of the present disclosure.
[0021] The figures depict various embodiments of the disclosed
technology for purposes of illustration only, wherein the figures
use like reference numerals to identify like elements. One skilled
in the art will readily recognize from the following discussion
that alternative embodiments of the structures and methods
illustrated in the figures can be employed without departing from
the principles of the disclosed technology described herein.
DETAILED DESCRIPTION
Approaches for Notification Send Control Using Negative
Sentiment
[0022] Users often utilize computing devices for a wide variety of
purposes. For example, users of a social networking system can use
their computing devices to interact with one another, access
content, share content, and create content. The social networking
system can deliver various notifications to these users. For
example, a given user of the social networking system can
administer a page on the social networking system. The social
networking system can deliver to this user a notification which
encourages the user to perform one or more specified actions with
regard to the page. As another example, a first user of the social
networking can follow a second user on the social networking
system. In this example, the social networking system can deliver
to the first user a notification which informs the first user that
the second user has made a post.
[0023] By sending notifications, the social networking system can
provide the users of the social networking system with enhanced
experiences. For example, higher quality pages on the social
networking system can flow from notifications which encourage
users, such as page admins, to take actions which improve pages
which they administrate. As another example, a first user can more
easily learn of new posts made by second user when notifications
draw attention to the new posts. However, as one illustration, a
user who receives too many notifications from the social networking
can come to harbor negative sentiment towards the social networking
system or entities of the social networking system to which the
notifications may be attributed. Conventional approaches tend to
select and deliver to users notifications which the users are
predicted to enjoy. However, these approaches can tend to fail to
adequately address negative sentiment. As one illustration,
notifications of a sort which may prove enjoyable to a user in
small quantities can prove irritating when received by that user in
large quantities.
[0024] Due to these or other concerns, the aforementioned and other
conventional approaches specifically arising in the realm of
computer technology can be disadvantageous or problematic.
Therefore, an improved approach can be beneficial for addressing or
alleviating various drawbacks associated with conventional
approaches. Based on computer technology, the disclosed technology
can use negative sentiment in controlling deliveries of
notifications to users of a social networking system. In some
embodiments, the social networking system can receive a request to
deliver a notification to a user of the social networking system.
The social networking system can use one or more machine learning
models to determine a likelihood of the user rejecting the
notification. The social networking system can also use one or more
machine learning models to determine a likelihood of the user
selecting the notification. Subsequently, the social networking
system can calculate a score which considers the two likelihoods.
The social networking system can then compare the score to a
threshold. Where the score does not meet the threshold, the social
networking system does not deliver the notification to the user.
Where the score meets or exceeds the threshold, the social
networking system can deliver the notification to the user. More
details regarding the discussed technology are provided herein.
[0025] FIG. 1 illustrates an example system 100 including an
example notification control module 102, according to an embodiment
of the present disclosure. As shown in the example of FIG. 1, the
notification control module 102 can include a prediction module
104, a decision module 106, and a delivery module 108. In some
instances, the example system 100 can include at least one data
store 110. The components (e.g., modules, elements, etc.) shown in
this figure and all figures herein are exemplary only, and other
implementations can include additional, fewer, integrated, or
different components. Some components may not be shown so as not to
obscure relevant details. In some embodiments, the notification
control module 102 can be implemented in a system, such as a social
networking system. While the disclosed technology may be described
herein in connection with a social networking system for
illustrative purposes, the disclosed technology can be implemented
in any other type of system or environment.
[0026] In some embodiments, the notification control module 102 can
be implemented, in part or in whole, as software, hardware, or any
combination thereof. In general, a module as discussed herein can
be associated with software, hardware, or any combination thereof.
In some implementations, one or more functions, tasks, and/or
operations of modules can be carried out or performed by software
routines, software processes, hardware, and/or any combination
thereof. In some cases, the notification control module 102 can be
implemented, in part or in whole, as software running on one or
more computing devices or systems. For example, the notification
control module 102 or at least a portion thereof can be implemented
using one or more computing devices or systems that include one or
more servers, such as network servers or cloud servers. In another
example, the notification control module 102 or at least a portion
thereof can be implemented as or within an application (e.g., app),
a program, an applet, or an operating system, etc., running on a
user computing device or a client computing system, such as a user
device 610 of FIG. 6. In some instances, the notification control
module 102 can, in part or in whole, be implemented within or
configured to operate in conjunction with a system (or service),
such as a social networking system 630 of FIG. 6. The application
incorporating or implementing instructions for performing
functionality of the notification control module 102 can be created
by a developer. The application can be provided to or maintained in
a repository. In some cases, the application can be uploaded or
otherwise transmitted over a network (e.g., Internet) to the
repository. For example, a computing system (e.g., server)
associated with or under control of the developer of the
application can provide or transmit the application to the
repository. The repository can include, for example, an "app" store
in which the application can be maintained for access or download
by a user. In response to a command by the user to download the
application, the application can be provided or otherwise
transmitted over a network from the repository to a computing
device associated with the user. For example, a computing system
(e.g., server) associated with or under control of an administrator
of the repository can cause or permit the application to be
transmitted to the computing device of the user so that the user
can install and run the application. The developer of the
application and the administrator of the repository can be
different entities in some cases, but can be the same entity in
other cases. It should be understood that there can be many
variations or other possibilities.
[0027] The notification control module 102 can be configured to
communicate and/or operate with the at least one data store 110, as
shown in the example system 100. The at least one data store 110
can be configured to store and maintain various types of data. For
example, the data store 110 can store information used or generated
by the notification control module 102. The information used or
generated by the notification control module 102 can include, for
example, machine learning model persistence data, log data, and
lookup data. In some implementations, the at least one data store
110 can store information associated with the social networking
system (e.g., the social networking system 630 of FIG. 6). The
information associated with the social networking system can
include data about users, social connections, social interactions,
locations, geo-fenced areas, maps, places, events, pages, groups,
posts, communications, content, feeds, account settings, privacy
settings, a social graph, and various other types of data. In some
implementations, the at least one data store 110 can store
information associated with users, such as user identifiers, user
information, profile information, user specified settings, content
produced or posted by users, and various other types of user
data.
[0028] The prediction module 104 can generate a prediction that a
recipient will reject a notification. The recipient can include,
for example, an administrator of a page of a social networking
system. The prediction module 104 can also generate a prediction
that a recipient will select a notification. The prediction module
104 can be used to inform a decision of whether or not to deliver a
notification to a recipient. Additional details regarding the
prediction module 104 are provided below with reference to FIG.
2.
[0029] The decision module 106 can generate a decision as to
whether or not a notification should be delivered to a recipient.
The decision can be based on one or more machine learning models,
weights, and thresholds. Additional details regarding the decision
module 106 are provided below with reference to FIG. 3.
[0030] The delivery module 108 can deliver a notification to a
recipient. The recipient can be a user of a social networking
system. The delivery module 108 can receive a request to
potentially deliver the notification from a notification source. In
some embodiments, the notification source can be a process or
resource of a social networking system which selects or generates
notifications for users of the social networking system. In some
embodiments, the notification can be a panel. For instance, the
panel can provide a suggestion of one or more actions which the
recipient can perform with respect to a page administered by the
recipient. As one illustration, a suggested action can be adding an
image, or other media, to the page. As another illustration, a
suggested action can be adding a button to the page. In particular,
the suggestion can be to add a button which allows a page visitor
to place a food or merchandise order with a business affiliated
with the page. In some embodiments, the notification can be or
relate to a post. As an illustration, the post can be a post of a
page followed by the recipient, or a post of a user of the social
networking system whom the recipient follows. A channel of the
social networking system through which the notification can be
delivered can be selected. Examples of a channel of the social
networking system include a homepage, a feed (e.g., newsfeed), and
messaging-related communications. Many variations are possible.
[0031] The delivery module 108 can receive, from the decision
module 106, either an indication that the notification should be
delivered or an indication that the notification should not be
delivered. Where the delivery module 108 receives the indication
that the notification should not be delivered, the delivery module
108 does not deliver the notification. Where the delivery module
108 receives the indication that the notification should be
delivered, the delivery module 108 can deliver the notification to
the recipient. The notification to the recipient can delivered
through the selected channel.
[0032] The recipient of the notification can use an interface
presented by a user computing device to perform an action with
respect to the notification. The actions can include selecting the
notification and rejecting the notification. In some embodiments,
the recipient can also be able to choose to perform no action with
respect to the notification. Where the recipient selects the
notification, the interface can present to the recipient a
destination associated with the notification. As an example, where
the notification is a panel which provides a suggestion of one or
more actions to perform with respect to a page, the destination can
be the page. As another example, where the notification is or
relates to a post, the destination can be a page which authored the
post, or a profile of a user who authored the post. Many variations
are possible. Where the recipient rejects the notification, the
notification can be withdrawn. As one example, where the
notification is a panel and the channel is a homepage of the
recipient, the panel can be removed from the homepage. As another
example, where the notification is a post and the channel is a feed
of the recipient, the post can be removed from the feed. There can
be many variations or other possibilities. In some embodiments,
where the recipient chooses to perform no action with respect to
the notification, the notification can be retained.
[0033] In some embodiments, the delivery module 108 can maintain a
log of deliveries of notifications to recipients. In these
embodiments, after delivering the notification to the recipient,
the delivery module 108 can add an entry to the log. The entry can
indicate the recipient, the notification, and the channel. Further,
the delivery module 108 can maintain a log of actions taken by
recipients with regard to notifications. After the recipient has
performed an action with respect to the notification, the delivery
module 108 can add an entry to the log. The entry can indicate the
recipient, the notification, and the action chosen. The entry can
also indicate the channel used to deliver the notification. In some
embodiments, the prediction module 104 can use the log in training
or retraining one or more machine learning models used by the
prediction module 104, as discussed in more detail herein.
[0034] The delivery module 108 can calculate one or more metrics
regarding the notifications which it delivers to recipients. As one
example, the delivery module 108 can calculate a negative sentiment
metric. As examples, the negative sentiment metric can quantify
user feelings of annoyance, anger, irritation, and the like. Many
variations are possible. The delivery module 108 can calculate the
negative sentiment metric with respect to each of one or more of
the recipients. As one example, a negative sentiment metric for a
given recipient recipient can be calculated as
NS.sub.recipient:
NS recipient = k u = 0 n Xout u w u ( 1 ) ##EQU00001##
In the equation, u can be a given delivered unique notification,
and n can be the number of unique notifications which were
delivered. As an illustration, one unique notification can be a
panel suggesting that a particular action be performed for a page,
while a second unique notification can be a panel suggesting that a
different action be performed for the page. Also in the equation,
Xout.sub.u can be a number of times the given recipient has
rejected the unique notification u. Further in the equation,
w.sub.u can be a number of times the unique notification u was
delivered to the given recipient. Also in the equation, k can be a
constant. In some embodiments, k can be chosen through
experimentation. In some embodiments, other approaches can be used
to determine the negative sentiment metric for the recipient. For
example, the delivery module 108 can use an interface presented by
a user computing device to provide a survey to the recipient. The
survey can list one or more notifications delivered to the
recipient in the past. For each of the listed notifications, the
survey can ask the recipient to indicate whether or not the
notification caused the recipient to experience a negative
sentiment. Subsequently, the delivery module 108 can calculate the
negative sentiment metric for the recipient as a number of
notifications which caused negative sentiment, divided by a total
number of notifications sent to the recipient. Many variations are
possible.
[0035] As another example, the delivery module 108 can calculate a
location action metric. The delivery module 108 can calculate the
location action metric with respect to each of one or more of the
recipients, and with respect to each of one or more locations on
the social networking system. The location action metric can
quantify an extent to which a given recipient is active with
respect to a given location on the social networking system. As one
illustration, the given location can be a page on the social
networking system which is administered by the given recipient. As
another illustration, the given location can be a profile of a user
followed by the given recipient. As a further illustration, the
given location can be a page on the social networking system
followed by the given recipient. As one example, a location action
metric can be calculated for the given recipient recipient and the
given location location as:
LA recipient , location = j v = 0 m cli ck v q v ( 2 )
##EQU00002##
In the equation, v can be a given delivered unique notification
which has location as a destination. Also in the equation, m can be
a number of unique notifications delivered which have location as a
destination. Further in the equation, click.sub.v can be a number
of times the given recipient has selected the unique notification
v. Also in the equation, q.sub.u can be a number of times the
unique notification v was delivered to the given recipient. Further
in the equation, j can be a constant. In some embodiments, j can be
chosen through experimentation. Many variations are possible.
[0036] FIG. 2 illustrates an example prediction module 202,
according to an embodiment of the present disclosure. In some
embodiments, the prediction module 104 of FIG. 1 can be implemented
as the example prediction module 202. As shown in FIG. 2, the
prediction module 202 can include a rejection prediction module 204
and a selection prediction module 206.
[0037] The rejection prediction module 204 can access one or more
machine learning models suitably trained to provide predictions
regarding a likelihood of a recipient rejecting a notification. The
machine learning models can apply any generally known approach for
classification. In various embodiments, each of the machine
learning models can accept certain inputs and return certain
outputs. In one implementation, one type of input can include, for
example, feature data for a recipient. As one example, feature data
for the recipient can include a number of notifications the
recipient receives. As another example, feature data for the
recipient can include a kind of business with which the recipient
is affiliated. In some embodiments, the kind of business with which
the recipient is affiliated can be reflected by a page administered
by the recipient. As a further example, feature data for the
recipient can include a likelihood of the recipient to either
select or reject a notification within a preselected number of
days, or other time duration, after delivery. As an additional
example, feature data for the recipient can include kinds of posts
or stories created by the recipient. As yet another example,
feature data for the recipient can include kinds of posts or
stories read by the recipient. In some embodiments, feature data
indicating kinds of posts or stories can include feature data for
text and media of the posts or stories. Another type of input can
include, for example, feature data for a notification. As an
example, the feature data for the notification can include feature
data for text and media of the notification. As another example,
the feature data for the notification can include feature data
which relates to timing of the notification, such as a delivery
time of the notification. In some embodiments, yet another type of
input can include, for example, a feature vector which indicates,
for each of one or more channels of the social networking system,
whether or not the notification was previously delivered to the
recipient using the channel. Many variations are possible with
respect to the types of inputs and related feature data that can be
provided to a machine learning model. An output of the machine
learning model can be a prediction of a likelihood that the
recipient will reject the notification. In some embodiments, the
output of the machine learning model can provide a prediction of a
likelihood that the recipient will reject the notification if the
notification is delivered using a given channel of the social
networking system. As an illustration, the output of the machine
learning model can provide a prediction regarding delivery of the
notification using a feed. In some embodiments, the rejection
prediction module 204 can have access to a plurality of machine
learning models to predict a likelihood that the recipient will
reject the notification, and each of the plurality of machine
learning models can correspond to a particular channel of the
social networking system through which the notification can be
delivered.
[0038] The rejection prediction module 204 can determine a
likelihood that a recipient will reject a notification proposed for
potential delivery to the recipient. The rejection prediction
module 204 can provide inputs to one of the machine learning models
to determine the likelihood that the recipient will reject the
notification. In one implementation, the inputs can be, for
example, feature data for the recipient and feature data for the
notification. In some embodiments, a further input can be a feature
vector which indicates, for each of one or more channels of the
social networking system, whether the notification was previously
delivered to the recipient using the channel, as discussed above.
The rejection prediction module 204 can populate the vector using
the log of deliveries of notifications to recipients which is
maintained by the delivery module 108. The rejection prediction
module 204 can receive from the machine learning model an output
indicating a likelihood that the recipient will reject the
notification.
[0039] The selection prediction module 206 can access one or more
machine learning models suitably trained to provide predictions
regarding a likelihood of a recipient selecting a notification.
Each of the trained machine learning models can correspond to
notification delivery using a different channel of the social
networking system. In one implementation, each of the machine
learning models to which the selection prediction module 206 has
access can accept a variety of inputs and can return a variety of
outputs. The inputs can include, for example, feature data for a
recipient and feature data for a notification. In some embodiments,
a further input can be a feature vector regarding previous delivery
of the notification, as discussed in connection with the rejection
prediction module 204. An output of the machine learning model can
be a prediction of a likelihood that the recipient will select the
notification. In some embodiments, the output of the machine
learning model is associated with a particular channel of a social
networking system, and can provide a prediction of a likelihood
that the recipient will select the notification when the
notification is delivered through the particular channel.
[0040] The selection prediction module 206 can determine a
likelihood that a recipient selects a notification proposed for
potential delivery to the recipient. The selection prediction
module 206 can provide inputs to the machine learning model to
determine the likelihood that the recipient will select the
notification. In one implementation, the inputs to the machine
learning model can include, for example, feature data for the
recipient and feature data for the notification. In some
embodiments, the inputs can also include a feature vector which
regards previous delivery of the notification, as discussed above.
The selection prediction module 206 can receive an output from the
machine learning model indicating a likelihood that the recipient
will select the notification. One or more machine learning models
discussed in connection with the notification control module 102
and its components can be implemented separately or in combination,
for example, as a single machine learning model, as multiple
machine learning models, as one or more staged machine learning
models, as one or more combined machine learning models, etc.
[0041] FIG. 3 illustrates an example decision module 302, according
to an embodiment of the present disclosure. In some embodiments,
the decision module 106 of FIG. 1 can be implemented as the example
decision module 302. As shown in FIG. 3, the decision module 302
can include a selection module 304 and a calculation module
306.
[0042] The selection module 304 can select a machine learning model
to predict a likelihood of a recipient selecting a notification,
and a machine learning model to predict a likelihood of the
recipient rejecting the notification. The selection module 304 can
also select a weight a, and a threshold to decide whether or not
the notification should be delivered to the recipient. In some
embodiments, the selection module 304 can access references, such
as a first reference and a second reference, to determine machine
learning models, a value for a, and a value for a threshold. In
some embodiments, the first reference and the second reference can
be a first lookup table and a second lookup table. In one example,
the first reference can list one or more channels of the social
networking system. For each channel, the first reference can list a
machine learning model that can be used in determining a likelihood
of a recipient selecting a notification delivered using the channel
and a machine learning model that can be used in determining a
likelihood of a recipient rejecting a notification delivered using
the channel. In this example, the second reference can likewise
list one or more channels of the social networking system. For each
channel, the second reference can list a value for a and a value
for the threshold. In some embodiments, the values for a and the
values for the threshold of the second reference can be populated
by way of experimentation. As discussed, the delivery module 108
can calculate negative sentiment metrics and location action
metrics. Based on the experimentation, for each channel, a value
for a and a value for the threshold can be chosen. In some
embodiments, for a given channel, a value for a and a value for the
threshold can be chosen that achieve a predetermined level of the
local action metric while not exceeding a predetermined level of
the negative sentiment metric. Many variations are possible.
Accordingly, in relation to a proposed notification for a
particular recipient in a given channel, the selection module 304
can determine a machine learning model that can be used in
determining a likelihood of the recipient selecting the
notification delivered using the channel, a machine learning model
that can be used in determining a likelihood of the recipient
rejecting the notification delivered using the channel, a value for
a, and a value for the threshold.
[0043] The calculation module 306 can determine, with respect to a
particular notification and a particular recipient, whether or not
the notification should be delivered to the recipient. The
calculation module 306 can calculate a score and compare the score
to a threshold. In some embodiments, the score can be calculated as
follows:
eSelect-.alpha.eReject (3)
In this equation, eSelect can be a likelihood that the recipient
selects the notification; eReject can be a likelihood that the
recipient rejects the notification; and .alpha. can be a weight. As
reflected by the equation, the effect of eReject on the score can
be weighted by the magnitude of .alpha..
[0044] In some embodiments, the calculation module 306 can receive,
from the selection module 304, the specification of the machine
learning model that can be used in determining the likelihood of
the recipient selecting the notification, the machine learning
model that can be used in determining the likelihood of the
recipient rejecting the notification, a, and the threshold. The
calculation module 306 can receive, from the prediction module 202,
a determination of a likelihood that the recipient rejects the
notification and a determination of a likelihood that the recipient
selects the notification. In particular, the calculation module 306
can receive, from the prediction module 202 a numerical value for
the likelihood that the recipient rejects the notification and a
numerical value for the likelihood that the recipient selects the
notification.
[0045] The calculation module 306 can calculate the score using
equation (3). In calculating the score, the calculation module 306
can use the likelihood that the recipient rejects the notification
as eReject and can use the received likelihood that the recipient
selects the notification as eSelect. Also, in calculating the
score, the calculation module 306 can use the value of a. The
calculation module 306 can then compare a result of the
calculation, or the score, to the threshold. Where the score meets
or exceeds the threshold, the calculation module 306 can determine
that the notification should be delivered, and accordingly the
notification is delivered to the recipient. Where the result does
not meet the threshold, the calculation module 306 can determine
that the notification should not be delivered, and accordingly the
notification is not delivered to the recipient.
[0046] FIG. 4 illustrates an example functional block diagram 400,
according to an embodiment of the present disclosure. The
functional block diagram 400 illustrates an example of operation of
the notification control module 102, as discussed in more detail
above. A notification 402 for potential delivery to a recipient
through a particular channel of a social networking system is
shown. Various input data in relation to the notification 402 can
be provided to machine learning models 404, 406. The input data can
include, for example, feature data for the recipient, feature data
for the notification 402, and a feature vector regarding previous
delivery of the notification 402, as discussed above. The machine
learning model 404 and the machine learning model 406 can predict a
likelihood that the recipient will, respectively, reject and select
the notification 402. In some instances, the machine learning
models 404, 406 are specific to the particular channel through
which the notification 402 is to be delivered. Based on the input
data, the machine learning model 404 can generate a prediction
regarding a likelihood that the recipient will reject the
notification 402. The prediction can be expressed as a rejection
prediction value 408 corresponding to eReject, as discussed above
in connection with equation (3). Likewise, based on the input data,
the machine learning model 406 can generate a prediction regarding
a likelihood that the recipient will select the notification 402.
The prediction can be expressed as a selection prediction value 410
corresponding to eSelect, as discussed above in connection with
equation (3). A value for a 412 and a threshold value 418 can be
determined. As discussed in more detail above, the value for a 412
and the threshold value 418 can be based at least in part on a
desired levels of a local action metric and a negative sentiment
metric.
[0047] The rejection prediction value 408, the selection prediction
value 410, and the value for a 412 can be provided to score
calculation 414, where a score 416 according to equation (3) is
calculated. The score 416 can be compared with the threshold value
418. When the score 416 satisfies the threshold value 418, the
notification 402 can be delivered at block 420. When the score 416
does not satisfy the threshold value 418, the notification 402 is
not delivered at block 422.
[0048] FIG. 5 illustrates an example process 500, according to
various embodiments of the present disclosure. It should be
appreciated that there can be additional, fewer, or alternative
steps performed in similar or alternative orders, or in parallel,
within the scope of the various embodiments discussed herein unless
otherwise stated.
[0049] At block 502, the example process 500 can determine a
likelihood of a rejection of a notification proposed for delivery
to a recipient. At block 504, the process can perform a delivery
determination for the notification. Then, at block 506, the process
can deliver the notification to the recipient based on the delivery
determination.
[0050] It is contemplated that there can be many other uses,
applications, and/or variations associated with the various
embodiments of the present disclosure. For example, in some cases,
user can choose whether or not to opt-in to utilize the disclosed
technology. The disclosed technology can also ensure that various
privacy settings and preferences are maintained and can prevent
private information from being divulged. In another example,
various embodiments of the present disclosure can learn, improve,
and/or be refined over time.
Social Networking System--Example Implementation
[0051] FIG. 6 illustrates a network diagram of an example system
600 that can be utilized in various scenarios, in accordance with
an embodiment of the present disclosure. The system 600 includes
one or more user devices 610, one or more external systems 620, a
social networking system (or service) 630, and a network 650. In an
embodiment, the social networking service, provider, and/or system
discussed in connection with the embodiments described above may be
implemented as the social networking system 630. For purposes of
illustration, the embodiment of the system 600, shown by FIG. 6,
includes a single external system 620 and a single user device 610.
However, in other embodiments, the system 600 may include more user
devices 610 and/or more external systems 620. In certain
embodiments, the social networking system 630 is operated by a
social network provider, whereas the external systems 620 are
separate from the social networking system 630 in that they may be
operated by different entities. In various embodiments, however,
the social networking system 630 and the external systems 620
operate in conjunction to provide social networking services to
users (or members) of the social networking system 630. In this
sense, the social networking system 630 provides a platform or
backbone, which other systems, such as external systems 620, may
use to provide social networking services and functionalities to
users across the Internet.
[0052] The user device 610 comprises one or more computing devices
(or systems) that can receive input from a user and transmit and
receive data via the network 650. In one embodiment, the user
device 610 is a conventional computer system executing, for
example, a Microsoft Windows compatible operating system (OS),
macOS, and/or a Linux distribution. In another embodiment, the user
device 610 can be a computing device or a device having computer
functionality, such as a smart-phone, a tablet, a personal digital
assistant (PDA), a mobile telephone, a laptop computer, a wearable
device (e.g., a pair of glasses, a watch, a bracelet, etc.), a
camera, an appliance, etc. The user device 610 is configured to
communicate via the network 650. The user device 610 can execute an
application, for example, a browser application that allows a user
of the user device 610 to interact with the social networking
system 630. In another embodiment, the user device 610 interacts
with the social networking system 630 through an application
programming interface (API) provided by the native operating system
of the user device 610, such as iOS and ANDROID. The user device
610 is configured to communicate with the external system 620 and
the social networking system 630 via the network 650, which may
comprise any combination of local area and/or wide area networks,
using wired and/or wireless communication systems.
[0053] In one embodiment, the network 650 uses standard
communications technologies and protocols. Thus, the network 650
can include links using technologies such as Ethernet, 802.11,
worldwide interoperability for microwave access (WiMAX), 3G, 4G,
CDMA, GSM, LTE, digital subscriber line (DSL), etc. Similarly, the
networking protocols used on the network 650 can include
multiprotocol label switching (MPLS), transmission control
protocol/Internet protocol (TCP/IP), User Datagram Protocol (UDP),
hypertext transport protocol (HTTP), simple mail transfer protocol
(SMTP), file transfer protocol (FTP), and the like. The data
exchanged over the network 650 can be represented using
technologies and/or formats including hypertext markup language
(HTML) and extensible markup language (XML). In addition, all or
some links can be encrypted using conventional encryption
technologies such as secure sockets layer (SSL), transport layer
security (TLS), and Internet Protocol security (IPsec).
[0054] In one embodiment, the user device 610 may display content
from the external system 620 and/or from the social networking
system 630 by processing a markup language document 614 received
from the external system 620 and from the social networking system
630 using a browser application 612. The markup language document
614 identifies content and one or more instructions describing
formatting or presentation of the content. By executing the
instructions included in the markup language document 614, the
browser application 612 displays the identified content using the
format or presentation described by the markup language document
614. For example, the markup language document 614 includes
instructions for generating and displaying a web page having
multiple frames that include text and/or image data retrieved from
the external system 620 and the social networking system 630. In
various embodiments, the markup language document 614 comprises a
data file including extensible markup language (XML) data,
extensible hypertext markup language (XHTML) data, or other markup
language data. Additionally, the markup language document 614 may
include JavaScript Object Notation (JSON) data, JSON with padding
(JSONP), and JavaScript data to facilitate data-interchange between
the external system 620 and the user device 610. The browser
application 612 on the user device 610 may use a JavaScript
compiler to decode the markup language document 614.
[0055] The markup language document 614 may also include, or link
to, applications or application frameworks such as FLASH.TM. or
Unity.TM. applications, the Silverlight.TM. application framework,
etc.
[0056] In one embodiment, the user device 610 also includes one or
more cookies 616 including data indicating whether a user of the
user device 610 is logged into the social networking system 630,
which may enable modification of the data communicated from the
social networking system 630 to the user device 610.
[0057] The external system 620 includes one or more web servers
that include one or more web pages 622a, 622b, which are
communicated to the user device 610 using the network 650. The
external system 620 is separate from the social networking system
630. For example, the external system 620 is associated with a
first domain, while the social networking system 630 is associated
with a separate social networking domain. Web pages 622a, 622b,
included in the external system 620, comprise markup language
documents 614 identifying content and including instructions
specifying formatting or presentation of the identified content. As
discussed previously, it should be appreciated that there can be
many variations or other possibilities.
[0058] The social networking system 630 includes one or more
computing devices for a social network, including a plurality of
users, and providing users of the social network with the ability
to communicate and interact with other users of the social network.
In some instances, the social network can be represented by a
graph, i.e., a data structure including edges and nodes. Other data
structures can also be used to represent the social network,
including but not limited to databases, objects, classes, meta
elements, files, or any other data structure. The social networking
system 630 may be administered, managed, or controlled by an
operator. The operator of the social networking system 630 may be a
human being, an automated application, or a series of applications
for managing content, regulating policies, and collecting usage
metrics within the social networking system 630. Any type of
operator may be used.
[0059] Users may join the social networking system 630 and then add
connections to any number of other users of the social networking
system 630 to whom they desire to be connected. As used herein, the
term "friend" refers to any other user of the social networking
system 630 to whom a user has formed a connection, association, or
relationship via the social networking system 630. For example, in
an embodiment, if users in the social networking system 630 are
represented as nodes in the social graph, the term "friend" can
refer to an edge formed between and directly connecting two user
nodes.
[0060] Connections may be added explicitly by a user or may be
automatically created by the social networking system 630 based on
common characteristics of the users (e.g., users who are alumni of
the same educational institution). For example, a first user
specifically selects another user to be a friend. Connections in
the social networking system 630 are usually in both directions,
but need not be, so the terms "user" and "friend" depend on the
frame of reference. Connections between users of the social
networking system 630 are usually bilateral ("two-way"), or
"mutual," but connections may also be unilateral, or "one-way." For
example, if Bob and Joe are both users of the social networking
system 630 and connected to each other, Bob and Joe are each
other's connections. If, on the other hand, Bob wishes to connect
to Joe to view data communicated to the social networking system
630 by Joe, but Joe does not wish to form a mutual connection, a
unilateral connection may be established. The connection between
users may be a direct connection; however, some embodiments of the
social networking system 630 allow the connection to be indirect
via one or more levels of connections or degrees of separation.
[0061] In addition to establishing and maintaining connections
between users and allowing interactions between users, the social
networking system 630 provides users with the ability to take
actions on various types of items supported by the social
networking system 630. These items may include groups or networks
(i.e., social networks of people, entities, and concepts) to which
users of the social networking system 630 may belong, events or
calendar entries in which a user might be interested,
computer-based applications that a user may use via the social
networking system 630, transactions that allow users to buy or sell
items via services provided by or through the social networking
system 630, and interactions with advertisements that a user may
perform on or off the social networking system 630. These are just
a few examples of the items upon which a user may act on the social
networking system 630, and many others are possible. A user may
interact with anything that is capable of being represented in the
social networking system 630 or in the external system 620,
separate from the social networking system 630, or coupled to the
social networking system 630 via the network 650.
[0062] The social networking system 630 is also capable of linking
a variety of entities. For example, the social networking system
630 enables users to interact with each other as well as external
systems 620 or other entities through an API, a web service, or
other communication channels. The social networking system 630
generates and maintains the "social graph" comprising a plurality
of nodes interconnected by a plurality of edges. Each node in the
social graph may represent an entity that can act on another node
and/or that can be acted on by another node. The social graph may
include various types of nodes. Examples of types of nodes include
users, non-person entities, content items, web pages, groups,
activities, messages, concepts, and any other things that can be
represented by an object in the social networking system 630. An
edge between two nodes in the social graph may represent a
particular kind of connection, or association, between the two
nodes, which may result from node relationships or from an action
that was performed by one of the nodes on the other node. In some
cases, the edges between nodes can be weighted. The weight of an
edge can represent an attribute associated with the edge, such as a
strength of the connection or association between nodes. Different
types of edges can be provided with different weights. For example,
an edge created when one user "likes" another user may be given one
weight, while an edge created when a user befriends another user
may be given a different weight.
[0063] As an example, when a first user identifies a second user as
a friend, an edge in the social graph is generated connecting a
node representing the first user and a second node representing the
second user. As various nodes relate or interact with each other,
the social networking system 630 modifies edges connecting the
various nodes to reflect the relationships and interactions.
[0064] The social networking system 630 also includes
user-generated content, which enhances a user's interactions with
the social networking system 630. User-generated content may
include anything a user can add, upload, send, or "post" to the
social networking system 630. For example, a user communicates
posts to the social networking system 630 from a user device 610.
Posts may include data such as status updates or other textual
data, location information, images such as photos, videos, links,
music, or other similar data and/or media. Content may also be
added to the social networking system 630 by a third party. Content
"items" are represented as objects in the social networking system
630. In this way, users of the social networking system 630 are
encouraged to communicate with each other by posting text and
content items of various types of media through various
communication channels. Such communication increases the
interaction of users with each other and increases the frequency
with which users interact with the social networking system
630.
[0065] The social networking system 630 includes a web server 632,
an API request server 634, a user profile store 636, a connection
store 638, an action logger 640, an activity log 642, and an
authorization server 644. In an embodiment of the invention, the
social networking system 630 may include additional, fewer, or
different components for various applications. Other components,
such as network interfaces, security mechanisms, load balancers,
failover servers, management and network operations consoles, and
the like are not shown so as to not obscure the details of the
system.
[0066] The user profile store 636 maintains information about user
accounts, including biographic, demographic, and other types of
descriptive information, such as work experience, educational
history, hobbies or preferences, location, and the like that has
been declared by users or inferred by the social networking system
630. This information is stored in the user profile store 636 such
that each user is uniquely identified. The social networking system
630 also stores data describing one or more connections between
different users in the connection store 638. The connection
information may indicate users who have similar or common work
experience, group memberships, hobbies, or educational history.
Additionally, the social networking system 630 includes
user-defined connections between different users, allowing users to
specify their relationships with other users. For example,
user-defined connections allow users to generate relationships with
other users that parallel the users' real-life relationships, such
as friends, co-workers, partners, and so forth. Users may select
from predefined types of connections, or define their own
connection types as needed. Connections with other nodes in the
social networking system 630, such as non-person entities, buckets,
cluster centers, images, interests, pages, external systems,
concepts, and the like are also stored in the connection store
638.
[0067] The social networking system 630 maintains data about
objects with which a user may interact. To maintain this data, the
user profile store 636 and the connection store 638 store instances
of the corresponding type of objects maintained by the social
networking system 630. Each object type has information fields that
are suitable for storing information appropriate to the type of
object. For example, the user profile store 636 contains data
structures with fields suitable for describing a user's account and
information related to a user's account. When a new object of a
particular type is created, the social networking system 630
initializes a new data structure of the corresponding type, assigns
a unique object identifier to it, and begins to add data to the
object as needed. This might occur, for example, when a user
becomes a user of the social networking system 630, the social
networking system 630 generates a new instance of a user profile in
the user profile store 636, assigns a unique identifier to the user
account, and begins to populate the fields of the user account with
information provided by the user.
[0068] The connection store 638 includes data structures suitable
for describing a user's connections to other users, connections to
external systems 620 or connections to other entities. The
connection store 638 may also associate a connection type with a
user's connections, which may be used in conjunction with the
user's privacy setting to regulate access to information about the
user. In an embodiment of the invention, the user profile store 636
and the connection store 638 may be implemented as a federated
database.
[0069] Data stored in the connection store 638, the user profile
store 636, and the activity log 642 enables the social networking
system 630 to generate the social graph that uses nodes to identify
various objects and edges connecting nodes to identify
relationships between different objects. For example, if a first
user establishes a connection with a second user in the social
networking system 630, user accounts of the first user and the
second user from the user profile store 636 may act as nodes in the
social graph. The connection between the first user and the second
user stored by the connection store 638 is an edge between the
nodes associated with the first user and the second user.
Continuing this example, the second user may then send the first
user a message within the social networking system 630. The action
of sending the message, which may be stored, is another edge
between the two nodes in the social graph representing the first
user and the second user. Additionally, the message itself may be
identified and included in the social graph as another node
connected to the nodes representing the first user and the second
user.
[0070] In another example, a first user may tag a second user in an
image that is maintained by the social networking system 630 (or,
alternatively, in an image maintained by another system outside of
the social networking system 630). The image may itself be
represented as a node in the social networking system 630. This
tagging action may create edges between the first user and the
second user as well as create an edge between each of the users and
the image, which is also a node in the social graph. In yet another
example, if a user confirms attending an event, the user and the
event are nodes obtained from the user profile store 636, where the
attendance of the event is an edge between the nodes that may be
retrieved from the activity log 642. By generating and maintaining
the social graph, the social networking system 630 includes data
describing many different types of objects and the interactions and
connections among those objects, providing a rich source of
socially relevant information.
[0071] The web server 632 links the social networking system 630 to
one or more user devices 610 and/or one or more external systems
620 via the network 650. The web server 632 serves web pages, as
well as other web-related content, such as Java, JavaScript, Flash,
XML, and so forth. The web server 632 may include a mail server or
other messaging functionality for receiving and routing messages
between the social networking system 630 and one or more user
devices 610. The messages can be instant messages, queued messages
(e.g., email), text and SMS messages, or any other suitable
messaging format.
[0072] The API request server 634 allows one or more external
systems 620 and user devices 610 to call access information from
the social networking system 630 by calling one or more API
functions. The API request server 634 may also allow external
systems 620 to send information to the social networking system 630
by calling APIs. The external system 620, in one embodiment, sends
an API request to the social networking system 630 via the network
650, and the API request server 634 receives the API request. The
API request server 634 processes the request by calling an API
associated with the API request to generate an appropriate
response, which the API request server 634 communicates to the
external system 620 via the network 650. For example, responsive to
an API request, the API request server 634 collects data associated
with a user, such as the user's connections that have logged into
the external system 620, and communicates the collected data to the
external system 620. In another embodiment, the user device 610
communicates with the social networking system 630 via APIs in the
same manner as external systems 620.
[0073] The action logger 640 is capable of receiving communications
from the web server 632 about user actions on and/or off the social
networking system 630. The action logger 640 populates the activity
log 642 with information about user actions, enabling the social
networking system 630 to discover various actions taken by its
users within the social networking system 630 and outside of the
social networking system 630. Any action that a particular user
takes with respect to another node on the social networking system
630 may be associated with each user's account, through information
maintained in the activity log 642 or in a similar database or
other data repository. Examples of actions taken by a user within
the social networking system 630 that are identified and stored may
include, for example, adding a connection to another user, sending
a message to another user, reading a message from another user,
viewing content associated with another user, attending an event
posted by another user, posting an image, attempting to post an
image, or other actions interacting with another user or another
object. When a user takes an action within the social networking
system 630, the action is recorded in the activity log 642. In one
embodiment, the social networking system 630 maintains the activity
log 642 as a database of entries. When an action is taken within
the social networking system 630, an entry for the action is added
to the activity log 642. The activity log 642 may be referred to as
an action log.
[0074] Additionally, user actions may be associated with concepts
and actions that occur within an entity outside of the social
networking system 630, such as an external system 620 that is
separate from the social networking system 630. For example, the
action logger 640 may receive data describing a user's interaction
with an external system 620 from the web server 632. In this
example, the external system 620 reports a user's interaction
according to structured actions and objects in the social
graph.
[0075] Other examples of actions where a user interacts with an
external system 620 include a user expressing an interest in an
external system 620 or another entity, a user posting a comment to
the social networking system 630 that discusses an external system
620 or a web page 622a within the external system 620, a user
posting to the social networking system 630 a Uniform Resource
Locator (URL) or other identifier associated with an external
system 620, a user attending an event associated with an external
system 620, or any other action by a user that is related to an
external system 620. Thus, the activity log 642 may include actions
describing interactions between a user of the social networking
system 630 and an external system 620 that is separate from the
social networking system 630.
[0076] The authorization server 644 enforces one or more privacy
settings of the users of the social networking system 630. A
privacy setting of a user determines how particular information
associated with a user can be shared. The privacy setting comprises
the specification of particular information associated with a user
and the specification of the entity or entities with whom the
information can be shared. Examples of entities with which
information can be shared may include other users, applications,
external systems 620, or any entity that can potentially access the
information. The information that can be shared by a user comprises
user account information, such as profile photos, phone numbers
associated with the user, user's connections, actions taken by the
user such as adding a connection, changing user profile
information, and the like.
[0077] The privacy setting specification may be provided at
different levels of granularity. For example, the privacy setting
may identify specific information to be shared with other users;
the privacy setting identifies a work phone number or a specific
set of related information, such as, personal information including
profile photo, home phone number, and status. Alternatively, the
privacy setting may apply to all the information associated with
the user. The specification of the set of entities that can access
particular information can also be specified at various levels of
granularity. Various sets of entities with which information can be
shared may include, for example, all friends of the user, all
friends of friends, all applications, or all external systems 620.
One embodiment allows the specification of the set of entities to
comprise an enumeration of entities. For example, the user may
provide a list of external systems 620 that are allowed to access
certain information. Another embodiment allows the specification to
comprise a set of entities along with exceptions that are not
allowed to access the information. For example, a user may allow
all external systems 620 to access the user's work information, but
specify a list of external systems 620 that are not allowed to
access the work information. Certain embodiments call the list of
exceptions that are not allowed to access certain information a
"block list." External systems 620 belonging to a block list
specified by a user are blocked from accessing the information
specified in the privacy setting. Various combinations of
granularity of specification of information, and granularity of
specification of entities, with which information is shared are
possible. For example, all personal information may be shared with
friends whereas all work information may be shared with friends of
friends.
[0078] The authorization server 644 contains logic to determine if
certain information associated with a user can be accessed by a
user's friends, external systems 620, and/or other applications and
entities. The external system 620 may need authorization from the
authorization server 644 to access the user's more private and
sensitive information, such as the user's work phone number. Based
on the user's privacy settings, the authorization server 644
determines if another user, the external system 620, an
application, or another entity is allowed to access information
associated with the user, including information about actions taken
by the user.
[0079] In some embodiments, the social networking system 630 can
include a notification control module 646. The notification control
module 646 can, for example, be implemented as the notification
control module 102 of FIG. 1. In some embodiments, some or all of
the functionality and modules of the notification control module
646 (e.g., sub modules of the notification control module 102)
instead can be implemented in the user device 610.
Hardware Implementation
[0080] The foregoing processes and features can be implemented by a
wide variety of machine and computer system architectures and in a
wide variety of network and computing environments. FIG. 7
illustrates an example of a computer system 700 that may be used to
implement one or more of the embodiments described herein in
accordance with an embodiment of the invention. The computer system
700 includes sets of instructions for causing the computer system
700 to perform the processes and features discussed herein. The
computer system 700 may be connected (e.g., networked) to other
machines. In a networked deployment, the computer system 700 may
operate in the capacity of a server machine or a client machine in
a client-server network environment, or as a peer machine in a
peer-to-peer (or distributed) network environment. In an embodiment
of the invention, the computer system 700 may be the social
networking system 630, the user device 610, and the external system
620, or a component thereof. In an embodiment of the invention, the
computer system 700 may be one server among many that constitutes
all or part of the social networking system 630.
[0081] The computer system 700 includes a processor 702, a cache
704, and one or more executable modules and drivers, stored on a
computer-readable medium, directed to the processes and features
described herein. Additionally, the computer system 700 includes a
high performance input/output (I/O) bus 706 and a standard I/O bus
708. A host bridge 710 couples processor 702 to high performance
I/O bus 706, whereas I/O bus bridge 712 couples the two buses 706
and 708 to each other. A system memory 714 and one or more network
interfaces 716 couple to high performance I/O bus 706. The computer
system 700 may further include video memory and a display device
coupled to the video memory (not shown). Mass storage 718 and I/O
ports 720 couple to the standard I/O bus 708. The computer system
700 may optionally include a keyboard and pointing device, a
display device, or other input/output devices (not shown) coupled
to the standard I/O bus 708. Collectively, these elements are
intended to represent a broad category of computer hardware
systems, including but not limited to computer systems based on the
x86-compatible processors manufactured by Intel Corporation of
Santa Clara, Calif., and the x86-compatible processors manufactured
by Advanced Micro Devices (AMD), Inc., of Sunnyvale, Calif., as
well as any other suitable processor.
[0082] An operating system manages and controls the operation of
the computer system 700, including the input and output of data to
and from software applications (not shown). The operating system
provides an interface between the software applications being
executed on the system and the hardware components of the system.
Any suitable operating system may be used, such as the LINUX
Operating System, the Apple Macintosh Operating System, available
from Apple Inc. of Cupertino, Calif., UNIX operating systems,
Microsoft.RTM. Windows.RTM. operating systems, BSD operating
systems, and the like. Other implementations are possible.
[0083] The elements of the computer system 700 are described in
greater detail below. In particular, the network interface 716
provides communication between the computer system 700 and any of a
wide range of networks, such as an Ethernet (e.g., IEEE 802.3)
network, a backplane, etc. The mass storage 718 provides permanent
storage for the data and programming instructions to perform the
above-described processes and features implemented by the
respective computing systems identified above, whereas the system
memory 714 (e.g., DRAM) provides temporary storage for the data and
programming instructions when executed by the processor 702. The
I/O ports 720 may be one or more serial and/or parallel
communication ports that provide communication between additional
peripheral devices, which may be coupled to the computer system
700.
[0084] The computer system 700 may include a variety of system
architectures, and various components of the computer system 700
may be rearranged. For example, the cache 704 may be on-chip with
processor 702. Alternatively, the cache 704 and the processor 702
may be packed together as a "processor module," with processor 702
being referred to as the "processor core." Furthermore, certain
embodiments of the invention may neither require nor include all of
the above components. For example, peripheral devices coupled to
the standard I/O bus 708 may couple to the high performance I/O bus
706. In addition, in some embodiments, only a single bus may exist,
with the components of the computer system 700 being coupled to the
single bus. Moreover, the computer system 700 may include
additional components, such as additional processors, storage
devices, or memories.
[0085] In general, the processes and features described herein may
be implemented as part of an operating system or a specific
application, component, program, object, module, or series of
instructions referred to as "programs." For example, one or more
programs may be used to execute specific processes described
herein. The programs typically comprise one or more instructions in
various memory and storage devices in the computer system 700 that,
when read and executed by one or more processors, cause the
computer system 700 to perform operations to execute the processes
and features described herein. The processes and features described
herein may be implemented in software, firmware, hardware (e.g., an
application specific integrated circuit), or any combination
thereof.
[0086] In one implementation, the processes and features described
herein are implemented as a series of executable modules run by the
computer system 700, individually or collectively in a distributed
computing environment. The foregoing modules may be realized by
hardware, executable modules stored on a computer-readable medium
(or machine-readable medium), or a combination of both. For
example, the modules may comprise a plurality or series of
instructions to be executed by a processor in a hardware system,
such as the processor 702. Initially, the series of instructions
may be stored on a storage device, such as the mass storage 718.
However, the series of instructions can be stored on any suitable
computer readable storage medium. Furthermore, the series of
instructions need not be stored locally, and could be received from
a remote storage device, such as a server on a network, via the
network interface 716. The instructions are copied from the storage
device, such as the mass storage 718, into the system memory 714
and then accessed and executed by the processor 702. In various
implementations, a module or modules can be executed by a processor
or multiple processors in one or multiple locations, such as
multiple servers in a parallel processing environment.
[0087] Examples of computer-readable media include, but are not
limited to, recordable type media such as volatile and non-volatile
memory devices; solid state memories; floppy and other removable
disks; hard disk drives; magnetic media; optical disks (e.g.,
Compact Disk Read-Only Memory (CD ROMS), Digital Versatile Disks
(DVDs)); other similar non-transitory (or transitory), tangible (or
non-tangible) storage medium; or any type of medium suitable for
storing, encoding, or carrying a series of instructions for
execution by the computer system 700 to perform any one or more of
the processes and features described herein.
[0088] For purposes of explanation, numerous specific details are
set forth in order to provide a thorough understanding of the
description. It will be apparent, however, to one skilled in the
art that embodiments of the disclosure can be practiced without
these specific details. In some instances, modules, structures,
processes, features, and devices are shown in block diagram form in
order to avoid obscuring the description. In other instances,
functional block diagrams and flow diagrams are shown to represent
data and logic flows. The components of block diagrams and flow
diagrams (e.g., modules, blocks, structures, devices, features,
etc.) may be variously combined, separated, removed, reordered, and
replaced in a manner other than as expressly described and depicted
herein.
[0089] Reference in this specification to "one embodiment," "an
embodiment," "other embodiments," "one series of embodiments,"
"some embodiments," "various embodiments," or the like means that a
particular feature, design, structure, or characteristic described
in connection with the embodiment is included in at least one
embodiment of the disclosure. The appearances of, for example, the
phrase "in one embodiment" or "in an embodiment" in various places
in the specification are not necessarily all referring to the same
embodiment, nor are separate or alternative embodiments mutually
exclusive of other embodiments. Moreover, whether or not there is
express reference to an "embodiment" or the like, various features
are described, which may be variously combined and included in some
embodiments, but also variously omitted in other embodiments.
Similarly, various features are described that may be preferences
or requirements for some embodiments, but not other
embodiments.
[0090] The language used herein has been principally selected for
readability and instructional purposes, and it may not have been
selected to delineate or circumscribe the inventive subject matter.
It is therefore intended that the scope of the invention be limited
not by this detailed description, but rather by any claims that
issue on an application based hereon. Accordingly, the disclosure
of the embodiments of the invention is intended to be illustrative,
but not limiting, of the scope of the invention, which is set forth
in the following claims.
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